Clustering¶

Clustering algorithms are used to segment data or to find anomalies. Generally speaking, clustering algorithms are sensitive to unnormalized data, so it's important to properly prepare your data beforehand.

For example, if we consider the 'titanic' dataset, the features 'fare' and 'age' don't have values within the same interval; that is, 'fare' can be much higher than the 'age'. Applying a clustering algorithm to this kind of dataset would create misleading clusters.

To create a clustering model, we'll start by importing the k-means algorithm.

In [1]:

from verticapy.learn.cluster import KMeans

Next, we'll create a model object. Since Vertica has its own model management system, we just need to choose a model name and cluster size. The model's name must include the schema. By default, the public schema is used.

In [9]:

model = KMeans("KMeans_sm", n_cluster = 3)

Let's use the iris dataset to fit our model.

In [10]:

from verticapy.datasets import load_iris
iris = load_iris()

We can fit the model.

In [11]:

model.fit(iris, ["PetalLengthCm", "SepalLengthCm"])
model.plot()

Out[11]:

<AxesSubplot:xlabel='"PetalLengthCm"', ylabel='"SepalLengthCm"'>

While there aren't any real metrics for evaluating unsupervised models, metrics used during computation can help us to understand the quality of the model. For example, a k-means model with fewer clusters and when the k-means score, 'Between-Cluster SS / Total SS' is close to 1.

In [12]:

model.metrics_

Out[12]:

	value
	512.23072
	566.03207
	53.801351
	0.9049499969144859
	✅

Rows: 1-5 | Columns: 2

You can add the prediction to your vDataFrame.

In [13]:

model.predict(iris, name = "cluster")

Out[13]:

	123 Id Int	123 PetalLengthCm Numeric(6,3)	123 PetalWidthCm Numeric(6,3)	123 SepalLengthCm Numeric(6,3)	123 SepalWidthCm Numeric(6,3)	Abc Species Varchar(30)	123 cluster Integer
1	1	1.4	0.2	5.1	3.5	Iris-setosa	1
2	2	1.4	0.2	4.9	3.0	Iris-setosa	1
3	3	1.3	0.2	4.7	3.2	Iris-setosa	1
4	4	1.5	0.2	4.6	3.1	Iris-setosa	1
5	5	1.4	0.2	5.0	3.6	Iris-setosa	1
6	6	1.7	0.4	5.4	3.9	Iris-setosa	1
7	7	1.4	0.3	4.6	3.4	Iris-setosa	1
8	8	1.5	0.2	5.0	3.4	Iris-setosa	1
9	9	1.4	0.2	4.4	2.9	Iris-setosa	1
10	10	1.5	0.1	4.9	3.1	Iris-setosa	1
11	11	1.5	0.2	5.4	3.7	Iris-setosa	1
12	12	1.6	0.2	4.8	3.4	Iris-setosa	1
13	13	1.4	0.1	4.8	3.0	Iris-setosa	1
14	14	1.1	0.1	4.3	3.0	Iris-setosa	1
15	15	1.2	0.2	5.8	4.0	Iris-setosa	1
16	16	1.5	0.4	5.7	4.4	Iris-setosa	1
17	17	1.3	0.4	5.4	3.9	Iris-setosa	1
18	18	1.4	0.3	5.1	3.5	Iris-setosa	1
19	19	1.7	0.3	5.7	3.8	Iris-setosa	1
20	20	1.5	0.3	5.1	3.8	Iris-setosa	1
21	21	1.7	0.2	5.4	3.4	Iris-setosa	1
22	22	1.5	0.4	5.1	3.7	Iris-setosa	1
23	23	1.0	0.2	4.6	3.6	Iris-setosa	1
24	24	1.7	0.5	5.1	3.3	Iris-setosa	1
25	25	1.9	0.2	4.8	3.4	Iris-setosa	1
26	26	1.6	0.2	5.0	3.0	Iris-setosa	1
27	27	1.6	0.4	5.0	3.4	Iris-setosa	1
28	28	1.5	0.2	5.2	3.5	Iris-setosa	1
29	29	1.4	0.2	5.2	3.4	Iris-setosa	1
30	30	1.6	0.2	4.7	3.2	Iris-setosa	1
31	31	1.6	0.2	4.8	3.1	Iris-setosa	1
32	32	1.5	0.4	5.4	3.4	Iris-setosa	1
33	33	1.5	0.1	5.2	4.1	Iris-setosa	1
34	34	1.4	0.2	5.5	4.2	Iris-setosa	1
35	35	1.5	0.1	4.9	3.1	Iris-setosa	1
36	36	1.2	0.2	5.0	3.2	Iris-setosa	1
37	37	1.3	0.2	5.5	3.5	Iris-setosa	1
38	38	1.5	0.1	4.9	3.1	Iris-setosa	1
39	39	1.3	0.2	4.4	3.0	Iris-setosa	1
40	40	1.5	0.2	5.1	3.4	Iris-setosa	1
41	41	1.3	0.3	5.0	3.5	Iris-setosa	1
42	42	1.3	0.3	4.5	2.3	Iris-setosa	1
43	43	1.3	0.2	4.4	3.2	Iris-setosa	1
44	44	1.6	0.6	5.0	3.5	Iris-setosa	1
45	45	1.9	0.4	5.1	3.8	Iris-setosa	1
46	46	1.4	0.3	4.8	3.0	Iris-setosa	1
47	47	1.6	0.2	5.1	3.8	Iris-setosa	1
48	48	1.4	0.2	4.6	3.2	Iris-setosa	1
49	49	1.5	0.2	5.3	3.7	Iris-setosa	1
50	50	1.4	0.2	5.0	3.3	Iris-setosa	1
51	51	4.7	1.4	7.0	3.2	Iris-versicolor	0
52	52	4.5	1.5	6.4	3.2	Iris-versicolor	2
53	53	4.9	1.5	6.9	3.1	Iris-versicolor	0
54	54	4.0	1.3	5.5	2.3	Iris-versicolor	2
55	55	4.6	1.5	6.5	2.8	Iris-versicolor	2
56	56	4.5	1.3	5.7	2.8	Iris-versicolor	2
57	57	4.7	1.6	6.3	3.3	Iris-versicolor	2
58	58	3.3	1.0	4.9	2.4	Iris-versicolor	2
59	59	4.6	1.3	6.6	2.9	Iris-versicolor	2
60	60	3.9	1.4	5.2	2.7	Iris-versicolor	2
61	61	3.5	1.0	5.0	2.0	Iris-versicolor	2
62	62	4.2	1.5	5.9	3.0	Iris-versicolor	2
63	63	4.0	1.0	6.0	2.2	Iris-versicolor	2
64	64	4.7	1.4	6.1	2.9	Iris-versicolor	2
65	65	3.6	1.3	5.6	2.9	Iris-versicolor	2
66	66	4.4	1.4	6.7	3.1	Iris-versicolor	2
67	67	4.5	1.5	5.6	3.0	Iris-versicolor	2
68	68	4.1	1.0	5.8	2.7	Iris-versicolor	2
69	69	4.5	1.5	6.2	2.2	Iris-versicolor	2
70	70	3.9	1.1	5.6	2.5	Iris-versicolor	2
71	71	4.8	1.8	5.9	3.2	Iris-versicolor	2
72	72	4.0	1.3	6.1	2.8	Iris-versicolor	2
73	73	4.9	1.5	6.3	2.5	Iris-versicolor	2
74	74	4.7	1.2	6.1	2.8	Iris-versicolor	2
75	75	4.3	1.3	6.4	2.9	Iris-versicolor	2
76	76	4.4	1.4	6.6	3.0	Iris-versicolor	2
77	77	4.8	1.4	6.8	2.8	Iris-versicolor	0
78	78	5.0	1.7	6.7	3.0	Iris-versicolor	0
79	79	4.5	1.5	6.0	2.9	Iris-versicolor	2
80	80	3.5	1.0	5.7	2.6	Iris-versicolor	2
81	81	3.8	1.1	5.5	2.4	Iris-versicolor	2
82	82	3.7	1.0	5.5	2.4	Iris-versicolor	2
83	83	3.9	1.2	5.8	2.7	Iris-versicolor	2
84	84	5.1	1.6	6.0	2.7	Iris-versicolor	2
85	85	4.5	1.5	5.4	3.0	Iris-versicolor	2
86	86	4.5	1.6	6.0	3.4	Iris-versicolor	2
87	87	4.7	1.5	6.7	3.1	Iris-versicolor	2
88	88	4.4	1.3	6.3	2.3	Iris-versicolor	2
89	89	4.1	1.3	5.6	3.0	Iris-versicolor	2
90	90	4.0	1.3	5.5	2.5	Iris-versicolor	2
91	91	4.4	1.2	5.5	2.6	Iris-versicolor	2
92	92	4.6	1.4	6.1	3.0	Iris-versicolor	2
93	93	4.0	1.2	5.8	2.6	Iris-versicolor	2
94	94	3.3	1.0	5.0	2.3	Iris-versicolor	2
95	95	4.2	1.3	5.6	2.7	Iris-versicolor	2
96	96	4.2	1.2	5.7	3.0	Iris-versicolor	2
97	97	4.2	1.3	5.7	2.9	Iris-versicolor	2
98	98	4.3	1.3	6.2	2.9	Iris-versicolor	2
99	99	3.0	1.1	5.1	2.5	Iris-versicolor	1
100	100	4.1	1.3	5.7	2.8	Iris-versicolor	2

Rows: 1-100 | Columns: 7

This concludes this lesson on clustering models in VerticaPy. We'll look at time series models in the next lesson.